Personnel Information

写真a

HASE Takeshi


Job title

Project Professor

Birth date

1976
To the head of this page.▲

Graduating School 【 display / non-display 】

  • Keio University, Faculty of Science and Engineering, Department of Physics, 2000, Graduated

  • Keio University, Graduate School of Science and Technology, School of Fundamental Science and Technology, 2002, Graduated

  • Tokyo Medical and Dental University, 2008, Graduated

To the head of this page.▲

Degree 【 display / non-display 】

  • Ph. D, Tokyo Medical and Dental University

To the head of this page.▲

Campus Career 【 display / non-display 】

  • 2010.12
    -
    2015.03
    Tokyo Medical and Dental University， Medical Research Institute， Project Assistant Professor
  • 2017.11
    -
    2018.03
    Tokyo Medical and Dental University， -， Project Associate Professor
  • 2018.04
    -
    2021.03
    Tokyo Medical and Dental University， -， Project Associate Professor
  • 2021.04
    -
    2023.05
    Tokyo Medical and Dental University， Institute of Education， Innovative Human Resource Development Division， Project Associate Professor
  • 2023.06
    -
    Now
    Tokyo Medical and Dental University， Institute of Education， Innovative Human Resource Development Division， Project Professor
To the head of this page.▲

External Career 【 display / non-display 】

  • 2000.04
    -
    2002.03
    Keio University Science and Technology, teaching assistant
  • 2005.04
    -
    2010.01
    Saisei-kai Kawaguchi nurses' school, part-time lecturer
  • 2006.04
    -
    2007.03
    Kawaijuku Educational Institution, fellow
  • 2006.05
    -
    2008.03
    Tokyo Medical and Dental University, research assistant
  • 2007.04
    -
    2010.01
    Nihon University School of Medicine, part-time lecturer
  • 2008.04
    -
    2010.01
    Tokyo Medical and Dental University, research assistant professor
  • 2010.02
    -
    2010.11
    Riken Plant Science Center, post doctoral fellow
  • 2010.12
    -
    2015.03
    Tokyo Medical and Dental University, research assistant professor
  • 2010.12
    -
    2017.10
    The Systems Biology Institute, researcher
  • 2011.04
    -
    2013.03
    Saise-kai Kagaguchi nurses' school, part-time lecturer
  • 2013.01
    -
    2017.10
    SBX,Inc., Research, Researcher
  • 2013.04
    -
    2018.03
    RIKEN, Integrated Medical Science Center Laboratory for Disease Systems Modeling, Visiting researcher
  • 2017.04
    -
    Now
    Next generation biomedical institute, Research, Researcher (adjunct)
  • 2017.10
    -
    2021.03
    Tokyo Medical and Dental University, Project Associate Professor
  • 2018.04
    -
    Now
    SBX Corp, research, Research scientist (part time)
  • 2021.03
    -
    Now
    SBX BioSciences co. ltd, Research, adjunct researcher
  • 2021.04
    -
    Now
    Tokyo Medical and Dental University, Project Associate Professor

▼display all

To the head of this page.▲

Academic Society Affiliations 【 display / non-display 】

  • Japanese Society of Toxicologic Pathology

To the head of this page.▲

Research Areas 【 display / non-display 】

  • Statistical science

  • Biophysics

  • Life, health and medical informatics

  • System genome science

  • Genome biology

To the head of this page.▲
 

Published Papers & Misc 【 display / non-display 】

  1. Kubo A, Masugi Y, Hase T, Nagashima K, Kawai Y, Takizawa M, Hishiki T, Shiota M, Wakui M, Kitagawa Y, Kabe Y, Sakamoto M, Yachie A, Hayashida T, Suematsu M.. Polysulfide Serves as a Hallmark of Desmoplastic Reaction to Differentially Diagnose Ductal Carcinoma In Situ and Invasive Breast Cancer by SERS Imaging Antioxidants . 2023.01; 12 (2): 240. ( PubMed, DOI )

  2. Abe D, Inaji M, Hase T, Takahashi S, Sakai R, Ayabe F, Tanaka Y, Otomo Y, Maehara T. A Prehospital Triage System to Detect Traumatic Intracranial Hemorrhage Using Machine Learning Algorithms. JAMA network open. 2022.06; 5 (6): e2216393. ( PubMed, DOI )

  3. Polouliakh N, Hase T, Ghosh S, Kitano H. Toxicity Analysis of Pentachlorophenol Data with a Bioinformatics Tool Set. Methods in molecular biology (Clifton, N.J.). 2022; 2486 105-125. ( PubMed, DOI )

  4. Katsuda T, Sato N, Mogushi K, Hase T, Muramatsu M. Sub-GOFA: A tool for Sub-Gene Ontology function analysis in clonal mosaicism using semantic (logical) similarity. Bioinformation. 2022; 18 (1): 53-60. ( PubMed, DOI )

  5. (*Takeshi Hase is a part of, FANTOM consortium), Grapotte M, Saraswat M, Bessière C, Menichelli C, Ramilowski JA, Severin J, Hayashizaki Y, Itoh M, Tagami M, Murata M, Kojima-Ishiyama M, Noma S, Noguchi S, Kasukawa T, Hasegawa A, Suzuki H, Nishiyori-Sueki H, Frith MC, * FANTOM consortium, Chatelain C, Carninci P, de Hoon MJL, Wasserman WW, Bréhélin L, Lecellier CH. Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network Nature Communications. 2021.06; 12, 3297. ( DOI )

  6. Nishimura T, Nakamura H, Yachie A, Hase T, Fujii K, Koizumi H, Naruki S, Takagi M, Matsuoka Y, Furuya N, Kato H, Saji H. Disease-related cellular protein networks differentially affected under different EGFR mutations in lung adenocarcinoma. Scientific reports. 2020.12; 10 (1): 10881. ( PubMed, DOI )

  7. (Book Chapter) "Chapter 13. Cancer Network Medicine". - Network Medicine: Complex Systems in Human Disease and Therapeutics (Joseph Loscalzo, Albert-László Barabási, and Edwin K. Silverman (Eds.)) 2017.12; 294-323. ( DOI )

  8. Caron E, Roncagalli R, Hase T, Wolski WE, Choi M, Menoita MG, Durand S, García-Blesa A, Fierro-Monti I, Sajic T, Heusel M, Weiss T, Malissen M, Schlapbach R, Collins BC, Ghosh S, Kitano H, Aebersold R, Malissen B, Gstaiger M. Precise Temporal Profiling of Signaling Complexes in Primary Cells Using SWATH Mass Spectrometry. Cell reports. 2017.03; 18 (13): 3219-3226. ( PubMed, DOI )

  9. Hu Y, Hase T, Li HP, Prabhakar S, Kitano H, Ng SK, Ghosh S, Wee LJ. A machine learning approach for the identification of key markers involved in brain development from single-cell transcriptomic data. BMC genomics. 2016.12; 17 (Suppl 13): 1025-29. ( PubMed, DOI )

  10. Effect of placebo and lorazepam on functional connectivity in fearful vocal processing: an fMRI study 2016.06; 19 (Suppl_1): 54. ( DOI )

  11. Inferring causal molecular networks: empirical assessment through a community-based effort 2016.04; 13 (4): 310-318. ( DOI )

  12. Hase T, Kikuchi K, Ghosh S, Kitano H, Tanaka H. A computational approach to prioritize drug-target genes in the human protein interaction network The proceedings of The 21st International Symposium on Artificial Life and Robotics 2016 (AROB 21st 2016). 2016.01; 871-876.

  13. Kawakami E, Singh VK, Matsubara K, Ishii T, Matsuoka Y, Hase T, Kulkarni P, Siddiqui K, Kodilkar J, Danve N, Subramanian I, Katoh M, Shimizu-Yoshida Y, Ghosh S, Jere A, Kitano H. Network analyses based on comprehensive molecular interaction maps reveal robust control structures in yeast stress response pathways. NPJ systems biology and applications. 2016; 2 15018. ( PubMed, DOI )

  14. Hase T, Kikuchi K, Ghosh S, Kitano H, Tanaka H. Controllability of protein-protein interaction networks and their relationships with drug-targets, essential genes, and degree connectivities. The proceedings of The 20th International Symposium on Artificial Life and Robotics 2015 (AROB 20th 2015). 2015.01; 813-818.

  15. Takeshi Hase, Kaito Kikuchi, Samik Ghosh, Hiroaki Kitano, Hiroshi Tanaka. (Research article) Identification of drug-target modules in the human protein–protein interaction network Artificial Life and Robotics. 2014.12; 19 (4): 406-413. ( DOI )

  16. Matsuoka Y, Matsumae H, Katoh M, Eisfeld AJ, Neumann G, Hase T, Ghosh S, Shoemaker JE, Lopes TJ, Watanabe T, Watanabe S, Fukuyama S, Kitano H, Kawaoka Y. A comprehensive map of the influenza A virus replication cycle. BMC systems biology. 2013.10; 7 (97): 97. ( PubMed, DOI )

  17. Hase T, Ghosh S, Yamanaka R, Kitano H. Harnessing diversity towards the reconstructing of large scale gene regulatory networks. PLoS computational biology. 2013; 9 (11): e1003361. ( PubMed, DOI )

  18. (Book Chapter) "Chapter 20. Protein-Protein Interaction Networks: Structures, Evolution, and Application to Drug Design" - Protein-Protein Interactions - Computational and Experimental Tools (W. Cai, H. Hong (eds.)) 2012.03; 405-426. ( DOI )

  19. Hanada K, Hase T, Toyoda T, Shinozaki K, Okamoto M. Origin and evolution of genes related to ABA metabolism and its signaling pathways. Journal of plant research. 2011.07; 124 (4): 455-65. ( PubMed, DOI )

  20. Hase T, Niimura Y, Tanaka H. Difference in gene duplicability may explain the difference in overall structure of protein-protein interaction networks among eukaryotes. BMC evolutionary biology. 2010.11; 10 358. ( PubMed, DOI )

  21. Hase T, Tanaka H, Suzuki Y, Nakagawa S, Kitano H. Structure of protein interaction networks and their implications on drug design. PLoS computational biology. 2009.10; 5 (10): e1000550. ( PubMed, DOI )

  22. Hase T, Niimura Y, Kaminuma T, Tanaka H. Non-uniform survival rate of heterodimerization links in the evolution of the yeast protein-protein interaction network. PloS one. 2008.02; 3 (2): e1667. ( PubMed, DOI )

  23. 長谷 武志, 荻島 創一, 中川 草, 田中 博. 1P301 タンパク質間相互作用ネットワークのトポロジー構造における構造決定因子について(数理生物学)) 生物物理. 2005; 45 S107. ( DOI )

▼display all

To the head of this page.▲

Books etc 【 display / non-display 】

  1. Takeshi Hase, Samik Ghosh, Sucheendra K. Palaniappan, Hiroaki Kitano. Network Medicine - Complex Systems in Human Disease and Therapeutics (Joseph Loscalzo, Albert-László Barabási, and Edwin K. Silverman (Eds.)). Harvard University Press， 2017.01 Chapter 13. Cancer Network Medicine (ISBN : 9780674436534)

To the head of this page.▲

Conference Activities & Talks 【 display / non-display 】

  1. Takeshi Hase. Artificial intelligence based drug-target repositioning across diverse diseases. 7th International Symposium on BioComplexity 2022.01.25

  2. 辻 真吾, 油谷 浩幸, 長谷 武志, 田中 博. 創薬と癌研究のためのGraph Neural Networkの応用. 日本癌学会総会記事 2021.09.01

  3. AI and network analysis based computational framework for drug-target repositioning. 2021.01.23

  4. Takeshi Hase, Masanori Shimono. Neural network embedding of real neuronal networks. NETsciX 2020 2020.01.20

  5. Takeshi Hase, Samik Ghosh, Ken-ichi Aisaki, Satoshi Kitajima, Jun Kanno, Hiroaki Kitano. (招待講演)DTOX: Deep neural network-based computational framework to analyze omics data in Toxicology. OPENTOX ASIA 2018 2018.05.25 Asahi Seimei Otemachi Building, Tokyo, Japan

  6. 長谷 武志. (招待講演) Lecture 05: Application of machine learning methods to drug discovery. 1st Big Data Machine Learning in Healthcare in Japan 2018.02.25

  7. Takashi Hase, Shingo Tsuji, Kazuro Shimokawa, Hiroshi Tanaka. (Peer reviewed) Application of Deep Learning to Drug Discovery. Workshop on Artificial Life and Robotics in Busan 2017.09.08

  8. 辻 真吾, 長谷 武志, 田中 博, 油谷 浩幸. タンパク相互作用ネットワークを用いた新しいがん治療のためのAI創薬. 日本癌学会総会記事 2017.09.01

  9. Takeshi Hase, Samik Ghosh, Ayako Yachie, Hiroaki Kitano. (Peer reviewed) A neural network based text mining approach for inference of protein-protein interaction networks.. 2nd International Symposium on BioComplexity 2017.01.20 B-Con PLAZA, Beppu, JAPAN

  10. Yongli Hu, Takeshi Hase, Huipeng Li, Shyam Prabhakar, Hiroaki Kitano, See Kiong Ng, Samik Ghosh, Lawrence Jin, Kiat Wee. (Peer-reviewed) A machine learning approach for the identification of key markers involved in brain development from single-cell transcriptomic data.. 15th International Conference on Bioinformatics (InCOB 2016) 2016.09.21 Singapore

  11. Takeshi Hase, Kaito Kikuchi, Samik Ghosh, Hiroshi Tanaka, Hiroaki Kitano. (Peer reviewed) A computational approach to prioritize drug-target genes in the human protein interaction network. 1st International Symposium on BioComplexity 2016.01.21 B-Con PLAZA, Beppu, JAPAN

  12. Takeshi Hase, Kaito Kikuchi, Samik Ghosh, Hiroaki Kitano, Hiroshi Tanaka. (peer-reviewed) Controllability of protein-protein interaction networks and their relationships with drug-targets, essential genes, and degree connectivities. International Symposium on Artificial Life and Robotics AROB 20th 2015 2015.01.21

  13. Kaito Kikuchi, Takeshi Hase, Samik Ghosh, Hiroaki Kitano. (peer-reviewed) A network guided approach towards identification of novel drug targets in MRSA. 8th Asian Young Researchers Conference on Computational and Omics Biology (AYRCOB 2015.01.19

  14. Takeshi Hase. Identification of drug-target modules in the human protein–protein interaction network. AROB 19th 2014.01.22 Oita, Japan

  15. Takeshi Hase, Kaito Kikuchi, Samik Ghosh, Hiroaki Kitano, Hiroshi Tanaka. (peer-reviewed) Identification of drug-target modules in the human protein–protein interaction network. International Symposium on Artificial Life and Robotics 2014.01.01 B-Con Plaza, Beppu, Japan

  16. 一般口演, Takeshi Hase, Yoshihito Niimura. (Peer-reviewed) Difference in gene duplicability may explain the difference in overall structure of protein-protein interaction networks among eukaryotes.. Society for Molecular Biology and Evolution 2012 2012.06.01 Dublin Ireland

▼display all

To the head of this page.▲
 

Campus class subject 【 display / non-display 】

  • [深層学習による医療画像解析AIモデルの実装とモデルのGUI化]研修，2022 - Now

To the head of this page.▲

Social Contribution 【 display / non-display 】

  • Program Committee of CBI annual meeting 2016，The Chem-Bio Informatics Society，CBI annual meeting 2016，Tower Hall Funabori (4-1-1 Funabori, Edogawa-ku, Tokyo)，2015.12.22 - 2016.10.27

  • (Session Chair) "Biological evolution" in The Twentieth International Symposium on Artificial Life and Robotics 2015 (AROB 20th 2015)，International Society of Artificial Life and Robotics，THE TWENTIETH INTERNATIONAL SYMPOSIUM ON ARTIFICIAL LIFE AND ROBOTICS (AROB 20th 2015)，B-Con Plaza, Beppu, Oita, JAPAN，2015.01.21 - 2015.01.23

To the head of this page.▲